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26 May 2022 Novel methodology for SRAF placement over a machine learning generated probability map
Yi-Ting Lin, Sean Shang-En Tseng, Iris Hui-Ru Jiang, James P. Shiely
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Proceedings Volume 12052, DTCO and Computational Patterning; 120520X (2022) https://doi.org/10.1117/12.2613681
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States
Abstract
Subresolution assist feature (SRAF) insertion is an effective way to improve the printability and lithographic process window of isolated and semi-isolated features. State-of-the-art works resort to machine learning to reduce the computational cost associated with model-based methods. However, the result of SRAF probability learning, a probability map, cannot be directly treated as SRAFs. Inserting an SRAF to each layout grid predicted as 1 leads to design rule violations. Therefore, the SRAF probability learning model can only guide SRAF insertion and needs special handling to place SRAFs. For SRAF placement, we observe that placing SRAFs at probability maxima might be too greedy so that the SRAF prediction is masked to a certain extent. Therefore, we propose a novel methodology for SRAF placement over a machine learning generated probability map. First, a clustering-based method generates an initial SRAF inserted layout based on the given probability map. Then, a two-stage SRAF legalization strategy modifies the positions of SRAFs to be compliant with design rules. Experimental results show that given a probability map, our methodology can generate an SRAF inserted layout which guarantees design rule violation freeness while maintaining competitive PV band and EPE.
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Yi-Ting Lin, Sean Shang-En Tseng, Iris Hui-Ru Jiang, and James P. Shiely "Novel methodology for SRAF placement over a machine learning generated probability map", Proc. SPIE 12052, DTCO and Computational Patterning, 120520X (26 May 2022); https://doi.org/10.1117/12.2613681
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KEYWORDS
SRAF
Photovoltaics
Machine learning
Lithography
Optical proximity correction
Source mask optimization
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